• Rmarkdown: cloned from https://github.com/micha-silver/ReLTER_demo
• Slides on website: https://micha-silver.github.io/index.html
• Youtube videos: (to be announced)

Start a new package based on a git repo.

New Project -> Version Control

Encourages collaboration “out of the box”.

https://github.com/micha-silver/ReLTER_demo

Basics of git:

• https://git-scm.com/book/en/v2/Git-Basics-Getting-a-Git-Repository
• https://r-bio.github.io/intro-git-rstudio/

Some online resources

• R for Data Science: https://r4ds.had.co.nz/index.html
• FasteR: https://github.com/matloff/fasteR
• Rseek: https://rseek.org/

RStudio hints

• Always work in an R project
• Use a git repo for collaboration
• Do not setwd()
• Do not save workspace to .Rdata
• Use Control+Shift+F10 often

Markdown versus R script

• Code is the same
• RMarkdown adds formatted text and outputs
• RMarkdown can produce output in html, pdf, and slides
• Perfect for reports and … demos

• General (statistics, spatial, ML etc.): CRAN: https://cran.r-project.org/
• BioConductor (biology, environment): https://www.bioconductor.org/install/
• Packages developed on github, i.e. ReLTER

Installing packages

# Install some standard spatial packages from CRAN
if (!require("sf", quietly = TRUE))
  install.packages("sf")
if (!require("terra", quietly = TRUE))
  install.packages("terra")

# package from Bioconductor
if (!require("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install()
BiocManager::install("EBImage")

Installing from github

# Install development package from github
if (!require("remotes", quietly = TRUE))
  install.packages("remotes")

if (!require("ReLTER", quietly = TRUE))
  remotes::install_github("ropensci/ReLTER")

After installing, we need to load the packages into this R session.

# Convenient way to load list of packages
pkg_list <- c("sf", "terra", "ReLTER", "tmap")
lapply(pkg_list, require, character.only = TRUE)

## [[1]]
## [1] TRUE
## 
## [[2]]
## [1] TRUE
## 
## [[3]]
## [1] TRUE
## 
## [[4]]
## [1] TRUE

Key packages

• Handling vector data: sf, terra
• Handling raster data: terra, stars
• Visualization: tmap, leaflet, ggmap, (plot)
• Old, deprecated packages: sp, rgdal, raster, rgeos

Online resources

• Geocomputation with R: https://geocompr.robinlovelace.net/
• Spatial Data Science: https://www.rspatial.org/
• A blog on R-spatial issues: https://r-spatial.org/
• Packages are maintained here: https://github.com/rspatial

An initial example

Use the geodata package for sample data. Get administrative boundaries (from GADM) and monthly precipitation rasters (from WorldClim) for Slovakia.

remotes::install_github("rspatial/geodata")
library(geodata)
slv <- gadm("Slovakia", level=2, path=tempdir())
# Convert to `sf` class for plotting
slv <- st_as_sf(slv)
slv_precip <- worldclim_country("Slovakia",
                                var = "prec", path = tempdir())

Display the data with tmap, and use OpenStreetMaps as background.

tmap_mode("view")
tm_basemap("OpenStreetMap.Mapnik") +
  tm_shape(slv) + tm_borders(col = "purple", lwd = 2) +
  tm_shape(slv_precip$SVK_wc2.1_30s_prec_1) + 
  tm_raster(palette = "YlGnBu", alpha=0.7)

Now a different month

tm_basemap("OpenStreetMap.Mapnik") +
  tm_shape(slv) + tm_borders(col = "purple", lwd = 2) +
  tm_shape(slv_precip$SVK_wc2.1_30s_prec_8) +
  tm_raster(palette = "YlGnBu", alpha=0.7)